From Learning-to-Match to Learning-to-Discriminate: Global Prototype Learning for Few-shot Relation Classification

Abstract

Few-shot relation classification has attracted great attention recently, and is regarded as an effective way to tackle the long-tail problem in relation classification. Most previous works on few-shot relation classification are based on learning-to-match paradigms, which focus on learning an effective universal matcher between the query and one target class prototype based on inner-class support sets. However, the learning-to-match paradigm focuses on capturing the similarity knowledge between query and class prototype, while fails to consider discriminative information between different candidate classes. Such information is critical especially when target classes are highly confusing and domain shifting exists between training and testing phases. In this paper, we propose the Global Transformed Prototypical Networks (GTPN), which learns to build a few-shot model to directly discriminate between the query and all target classes with both inner-class local information and inter-class global information. Such learning-to-discriminate paradigm can make the model concentrate more on the discriminative knowledge between all candidate classes, and therefore leads to better classification performance. We conducted experiments on standard FewRel benchmarks. Experimental results show that GTPN achieves very competitive performance on few-shot relation classification and reached the best performance on the official leaderboard of FewRel 2.0 (https://thunlp.github.io/2/fewrel2_da.html ).